Sun Chenxi, Liang Yike, Yang Xiao, Zhao Biying, Zhang Pengju, Liu Sirui, Yang Dongyi, Wu Teng, Zhang Jianwei, Guo Hong
State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics, and Center for Quantum Information Technology, Peking University, Beijing 100871, China.
School of Life Sciences, Peking University, Beijing 100871, China.
iScience. 2024 Jun 4;27(7):110167. doi: 10.1016/j.isci.2024.110167. eCollection 2024 Jul 19.
Advancing biomagnetic measurement capabilities requires a nuanced understanding of sensor performance beyond traditional metrics. This study introduces Biomagnetism Evaluation via Simulated Testing (BEST), a novel methodology combining a current dipole model simulating cardiac biomagnetic fields with a convolutional neural network. Our investigation reveals that optimal sensor array performance is achieved when sensors are in close proximity to the magnetic source, with a shorter effective domain. Contrary to common assumptions, the bottom edge length of the sensor has a negligible impact on array performance. BEST provides a versatile framework for exploring the influence of diverse technical indicators on biomagnetic sensor performance, offering valuable insights for sensor development and selection.
提升生物磁测量能力需要对传感器性能有超越传统指标的细致理解。本研究引入了通过模拟测试进行生物磁评估（BEST），这是一种将模拟心脏生物磁场的电流偶极子模型与卷积神经网络相结合的新方法。我们的研究表明，当传感器靠近磁源且有效域较短时，可实现最佳的传感器阵列性能。与常见假设相反，传感器的底边长度对阵列性能的影响可忽略不计。BEST为探索各种技术指标对生物磁传感器性能的影响提供了一个通用框架，为传感器的开发和选择提供了有价值的见解。
